As model we use a fixed VGG19 net trained on imagenet (to speed up the training process and reduce complexity) and an additional hidden layer with 124 nodes. Tab. REF  compares results with different weights \(\lambda \)  and shows that we can in fact debias the pretrained VGG net. The CelebA dataset is heavily skewed; around \(\sim {77\%}\)  of the images showing women are labeled as attractive, compared to \(\sim {23\%}\)  of men. If \(\lambda \)  is strong enough, the influence of the skew on the fairness disappears. The downside is the decrease of accuracy to only \(\sim {55\%}\)  as the TNRs for female and male are getting low. Please note, the comparison with Quadrianto [1]} is not too accurate as our baseline already has a lower accuracy.
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